Best 902 quotes in «data quotes» category

I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships.

I wonder what really goes on in the minds of Church leadership who know of the data concerning the Book of Abraham, the new data on the First Vision, etc.... It would tend to devastate the Church if a top leader were to announce the facts.

I wonder whether any other generation has seen such astounding revolutions of data and values as those through which we have lived. Scarcely anything material or established which I was brought up to believe was permanent and vital, has lasted. Everything I was sure or taught to be sure was impossible, has happened.

I would like people to be more aware of the fact that ultimately we are paying for things, and it's not just as privacy advocates point out that we're paying with our time and our data. We're also paying with money, because the hundreds of billions of dollars spent on advertising is just factored into the cost of the goods that we buy. It's all coming out of our pocket, just in a really roundabout way.

I would only have been too pleased if someone had asked me for my data. If you really believed in your data, you wouldn't mind someone looking at it. You should be able to respond that if you don't believe me go out and do the measurements yourself.

Just providing information about how bad things are, or the statistics and data on incarceration by themselves, does lead to more depression and resignation and is not empowering. The information has to be presented in a way that's linked to the piece about encouraging students to think critically and creatively about how they might respond to injustice, and how young people have responded to injustice in the past.

Lastly, the great uncertainty of all data in War is a peculiar difficulty, because all action must, to a certain extent, be planned in a mere twilight, which in addition not unfrequently — like the effect of a fog or moonshine — gives to things exaggerated dimensions and an unnatural appearance.

I would say that hardware is the bone of the head, the skull. The semiconductor is the brain within the head. The software is the wisdom and data is the knowledge.

Junk food, empty calories and carbs are the Big Data of the masses

Just as modern man consumes both too many calories and calories of no nutritional value, information workers eat data both in excess and from the wrong sources.

Knowing is not simply a material act, since the object that is known always conceals something beyond the empirical datum. All our knowledge, even the most simple, is always a minor miracle, since it can never be fully explained by the material instruments that we apply to it. In every truth there is something more than we would have expected, in the love that we receive there is always an element that surprises us.

Knowledge is organized data. For it to be shelved as human knowledge it's got to be predictable, repeatable, all the basics of scientific validation have to be there.

Knowledge is invariably a matter of degree: you cannot put your finger upon even the simplest datum and say this we know.

Language fails not because thought fails, but because no verbal symbols can do justice to the fullness and richness of thought. Ifwe are to continue talking about "data" in any other sense than as reflective distinctions, the original datum is always such a qualitative whole.

Let's say tomorrow that there was a president, that we elected a president that eliminated the bulk collection of data. Let's just say it happened. What do you think would happen? People are like 'the sky would fall. We would be overrun with jihadists.' Maybe we could rely on the Constitution. Maybe we could get warrants. ... If you make the warrant specific, there's no limit to what you can get through a warrant.

Librarians are more important than ever before ... are uniquely qualified to help all of us separate the digital wheat from the chaff, to help us understand the reliability of the data we encounter.

Life is made up of a series of judgments on insufficient data, and if we waited to run down all our doubts, it would flow past us.

Learn to love the data and, for heaven's sake, write well.

Make your copy straightforward to read, understand and use. Use easy words; those that are used for everyday speech. Use phrases that are not too imprecise and very understandable. Do not be too stuffy; remove pompous words and substitute them with plain words. Minimize complicated gimmicks and constructions. If you can't give the data directly and briefly, you must consider writing the copy again.

Listening to the data is important... but so is experience and intuition. After all, what is intuition at its best but large amounts of data of all kinds filtered through a human brain rather than a math model?

Looking at the data and at my drug use and evaluating it carefully just let me see that I wasn't special, but my drug use challenged what I thought about cocaine. Because I would accept when I would say, "What happened to that person?" and someone would say, "They started using cocaine...they went downhill..." I would just accept that, even though I had a different experience and all these other people had a different experience. But I would throw that out because I thought my experience was an aberration.

Making good judgments when one has complete data, facts, and knowledge is not leadership - it's bookkeeping

Management must provide employees with tools that will enable them to do their jobs better, and with encouragement to use these tools. In particular, they must collect data.

Marketers can target Sponsored Updates to any segment of our premium audience based on professional profile data across more than 225 million members.

MapReduce has become the assembly language for big data processing, and SnapReduce employs sophisticated techniques to compile SnapLogic data integration pipelines into this new big data target language. Applying everything we know about the two worlds of integration and Hadoop, we built our technology to directly fit MapReduce, making the process of connectivity and large scale data integration seamless and simple.

Mathematicians come to the solution of a problem by the simple arrangement of the data, and reducing the reasoning to such simple operations, to judgments so brief, that they never lose sight of the evidence that serves as their guide.

Mathematics may be compared to a mill of exquisite workmanship, which grinds you stuff of any degree of fineness; but, nevertheless, what you get out depends upon what you put in; and as the grandest mill in the world will not extract wheat-flour from peascods, so pages of formulae will not get a definite result out of loose data.

Mathematics alone make us feel the limits of our intelligence. For we can always suppose in the case of an experiment that it is inexplicable because we don't happen to have all the data. In mathematics we have all the data, brought together in the full light of demonstration, and yet we don't understand. We always come back to the contemplation of our human wretchedness. What force is in relation to our will, the impenetrable opacity of mathematics is in relation to our intelligence.

Maybe we, the cultural workers , could apply ourselves. We're not going to resolve it in this moment or even in this generation, but perhaps as some kind of agenda we could invite our writers and cultural workers to address the problem a little more responsibly, because people are suffering tremendously from a want of data.

Measurement has too often been the leitmotif of many investigations rather than the experimental examination of hypotheses. Mounds of data are collected, which are statistically decorous and methodologically unimpeachable, but conclusions are often trivial and rarely useful in decision making. This results from an overly rigorous control of an insignificant variable and a widespread deficiency in the framing of pertinent questions. Investigators seem to have settled for what is measurable instead of measuring what they would really like to know.

Merely presenting a driver's license or other document based on a birth certificate is not enough for an accurate verification. Biometric verification of identity must be made and then a data base of those persons who have legal status must be checked.

Microsoft is in a court battle with the Department of Justice. The DOJ is saying, "We want information from your data center in Ireland. It's not about a US citizen, but we want it." Microsoft said, "OK, fine. Go to a judge in Ireland. Ask them for a warrant. We have a mutual legal-assistance treaty. They'll do it. Give that to us, and we'll provide the information to you in accordance with Irish laws.

Modern statisticians are familiar with the notion that any finite body of data contains only a limited amount of information on any point under examination; that this limit is set by the nature of the data themselves, and cannot be increased by any amount of ingenuity expended in their statistical examination: that the statistician's task, in fact, is limited to the extraction of the whole of the available information on any particular issue.

Molecular genetics can show off some surprising relationships like, for example, the close relationship of whales to hippopotamuses, which I think nobody ever guessed until molecular data was looked at. The closest relatives of whales are hippopotamuses, even closer than any other cloven-hoofed animals.

Morphological information has provided the greatest single source of data in the formulation and development of the theory of evolution and that even now, when the preponderance of work is experimental, the basis for interpretation in many areas of study remains the form and relationships of structures.

Most companies don't want their data co-mingled with other customers. Small companies will tolerate it.

Most executives, many scientists, and almost all business school graduates believe that if you analyze data, this will give you new ideas. Unfortunately, this belief is totally wrong. The mind can only see what it is prepared to see.

Most managers receive much more data (if not information) than they can possibly absorb even if they spend all of their time trying to do so. Hence they already suffer from an information overload.

My advice was to start a policy of making reversible decisions before anyone left the meeting or the office. In a startup, it doesn't matter if you're 100 percent right 100 percent of the time. What matters is having forward momentum and a tight fact-based data/metrics feedback loop to help you quickly recognize and reverse any incorrect decisions. That's why startups are agile. By the time a big company gets the committee to organize the subcommittee to pick a meeting date, your startup could have made 20 decisions, reversed five of them and implemented the fifteen that worked.

My answer to someone who is in contrast with me - by not seeing God in the scientific data - is that you don't see God in the scientific data because you're not me. I have other experiences than you have, that bring me to look at this data as enriching my experience of God.

My investigations resembled the pursuit of the solution to a problem for which I had three data: the object, the thing connected with it in the shadow of my consciousness, and the light wherein that thing would become apparent.

My interest is not data, it's the world. And part of world development you can see in numbers. Others, like human rights, empowerment of women, it's very difficult to measure in numbers.

My radio show, I'd show up, I'd read the data, and I would have sound bites and stuff like that.

My study is NOT as a climatologist, but from a completely different perspective in which I am an expert … For decades, as a professional experimental test engineer, I have analyzed experimental data and watched others massage and present data. I became a cynic; My conclusion - 'if someone is aggressively selling a technical product who's merits are dependent on complex experimental data, he is likely lying'. That is true whether the product is an airplane or a Carbon Credit.

... negative feelings are not true feelings at all; rather, they are your thoughts about something, based always on the previous experience of yourself and others. You will not find Truth in your past data, only past data that is based on other past data that is based on other past data, and so forth. Forget your "past experience" and look directly at the experience you are having. Right Here, Right Now. There is your Truth.

New applications will have to deal with big data. We have to analyze it on the fly, so we have to have a system that is transactional and analytical at the same time. We cannot have a multi-stage system. This is too slow for modern applications.

Nilekani's technocratic obsession with gathering data is consistent with that of Bill Gates, as though lack of information is what is causing world hunger.

... no compelling data to support its anachronistic social Darwinism.

No great marketing decisions have ever been made on qualitative data

No matter how invasive the technologies at their disposal, marketers and pollsters never come to terms with the living process through which people choose products or candidates; they are looking at what people just bought or thought, and making calculations based on that after-the-fact data.